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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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The application of decision tree in the prediction of winning team

The application of decision tree in the prediction of winnin...

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2018 International Conference on Virtual Reality and intelligent systems, ICVRIS 2018

作者： Tang, Xiaohu Liu, Zhifeng Li, Taizhao Wu, Wenbin Wei, Zhenhua Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology East China University of Technology Nanchang Jiangxi330013 China Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of Technology Nanchang Jiangxi330013 China Information Engineering College East China University of Technology Nanchang Jiangxi330013 China

ISBN: (纸本)9781538680315

Decision tree algorithm is mainly used for analyzing data and *** algorithm is easy to understand and implement, and it is easy to evaluate the model through static *** this paper, the decision tree algorithm is applied to predict the result of Chinese Football Association Super League(CFASL). In this paper, the data of CFASL 2015, 2016 and 2017 seasons are collected. And 'the result of each team's last match', 'compare of teams' ranking in last season', 'the result of last time the two teams played',etc. are selected as features to predict the winner of a CFASL match. The experiments found that 'two teams' performance in their recent matches', 'whether team A has defeated team B as an away team' and 'whether team B has defeated team A as an away team' played a key role in improving the accuracy of predicting. © 2018 IEEE.

关键词： Forecasting

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Bidirectional LSTM-CRF for biomedical named entity recognition 14

Bidirectional LSTM-CRF for biomedical named entity recogniti...

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14th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Yang, Xuemin Gao, Zhihong Li, Yongmin Pan, Chuandi Yang, Ronggen Gong, Lejun Yang, Geng Jiangsu Key Laboratory of Big Data Security Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210003 China Zhejiang Engineering Research Center of Intelligence Medicine Wenzhou325035 China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China

ISBN: (纸本)9781538680971

Bio-medical entity recognition extracts significant entities, for instance cells, proteins and genes, which is an arduous task in an automatic system that mine knowledge in bioinformatics texts. In this thesis, we utilized a bidirectional long short-term memory (Bi-LSTM) combined with conditional random fields (CRFs) approach to automatically obtain word representation, obliterated the need for a marvelous number of feature engineering tasks. The consequences of this experiment represent the word representation method can effectually acquire potential semantic information. Without relying on any artificial features, the result on the test dataset obtained 76.81 % F-score. Therefore, the proposed method is expected to advance biomedical text mining in bioinformatics entity recognition. © 2018 IEEE.

关键词： Bioinformatics

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Chinese Named Entity Recognition in Power Domain Based on Bi-LSTM-CRF

Chinese Named Entity Recognition in Power Domain Based on Bi...

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作者： Zhenqiang Zhao Zhenyu Chen Jinbo Liu Yunhao Huang Xingyu Gao Fangchun Di Lixin Li Xiaohui Ji School of Information Engineering China University of Geosciences Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology(China Electric Power Research Institute) Big Data Center State Grid Corporation of China Power Dispatching and Control Center State Grid Corporation of China Institute of Microelectronics Chinese Academy of Sciences

Efficient recognition of proprietary entities is an important basic work for text data mining and intelligent application in power *** power domain Named Entity Recognition(NER) methods rely on feature engineering seriously,which unable to learn power entity features *** order to learn entity features automatically and extract power domain named entities efficiently,a model based on Bidirectional Long Short-Term Memory Neural Networks(Bi-LSTM) and Conditional Random Field(CRF) was proposed in this *** representations were fed into the neural networks as an additional feature and Skip-gram embeddings were trained on power domain *** results showed the precision rate reaches higher than 88.25% and the recalling rate reaches higher than 88.04%,which confirm the method based on BiLSTM and CRF is effective for named entity recognition in the power domain.

关键词： Named Entity Recognition Power Domain Bi-LSTM CRF

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Petri Net Control Method for Pipe-line systems and Its Implementation via CIF3 ⁎

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IFAC-PapersOnLine 2019年第24期52卷 261-266页

作者： YuHao Fu JiLiang Luo Huifeng Wu Jianhong Ye Yi-sheng Huang College Information Science and Engineering Huaqiao University Xiamen 361021 China College Information Science and Engineering Huaqiao University Xiamen 361021 China and also with Fujian Engineering Research Center of Motor Control and System Optimal Schedule Xiamen 361021 China Institute of Intelligent and Software Technology Hangzhou Dianzi University Hangzhou 310018 China Computer Science and Technology Huaqiao University Xiamen 361021 China and also with Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou 362000 China Department of Electrical Engineering National Ilan University Taiwan

As for a pipe-line system subjected to complex operations, an approach is proposed to synthesize the controller via Petri nets (PNs) such that the plant is run as concurrently as possible and the loads of equipments are balanced. A P-timed and labeled PN is designed to model the whole process of a pipe-line system, where tasks, including transporting material from one tank to another and cleaning tank, are represented by operational places, and level sensors amounted in tanks are represented by labels assigned to transitions. Further, monitor places are designed to resolve the conflict relations among tasks due to the shared valves and pipes. An method is presented to translate an PN controller into a CIF3 (Compositional Interchange Format) model, which can be converted into a PLC program in the CIF3 tool. A beer filtration plant is taken as an example to illustrate the approach, and its simulation experiments are carried out to verify the theoretic results.

关键词： Chemical plant Valve control Petri net CIF3

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Flexible, ultrathin bioelectronic materials and devices for chronically stable neural interfaces

Brain‐X

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Brain‐X 2023年第4期1卷

作者： Lianjie Zhou Zhongyuan Wu Mubai Sun Jaejin Park Mengdi Han Ming Wang Junsheng Yu Zengfeng Di Yongfeng Mei Wubin Bai Xinge Yu Ki Jun Yu Enming Song Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China Contribution: Investigation Visualization Writing - original draft Institute of Agro-food Technology Jilin Academy of Agricultural Sciences Changchun Jilin China Contribution: Data curation Investigation Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic Engineering Yonsei University Seoul Korea Department of Biomedical Engineering College of Future Technology Peking University Beijing China Contribution: Resources Validation State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai China State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Department of Material Science Fudan University Shanghai China Department of Applied Physical Sciences University of North Carolina at Chapel Hill Chapel Hill North Carolina USA Department of Biomedical Engineering City University of Hong Kong Hong Kong SAR China Wubin Bai Xinge Yu Ki Jun Yu and Enming Song. Contribution: Conceptualization Methodology Resources Supervision Writing - review & editing

Advanced technologies that can establish intimate, long-lived functional interfaces with neural systems have attracted increasing interest due to their wide-ranging applications in neuroscience, bioelectronic medicine, and the associated treatment of neurodegenerative diseases. A critical challenge of significance remains in the development of electronic platforms that offer conformal contact with soft brain tissue for the sensing or stimulation of brain activities and chronically stable operation in vivo, at scales that range from cellular-level resolution to macroscopic areas. This review summarizes recent advances in this field, with an emphasis on the use of demonstrated concepts, constituent materials, engineered designs, and system integration to address the current challenges. The article begins with an overview of recent bioelectronic platforms with unique form factors, ranging from filamentary probes to conformal sheets and three-dimensional frameworks for alleviating the mechanical mismatch between interface materials and neural tissues. Next, active interfaces which utilize inorganic/organic semiconductor-enabled devices are reviewed, highlighting various working principles of recording mechanisms including capacitively and conductively coupled sensing enabled by high transistor matrices at high spatiotemporal resolution. The subsequent section presents approaches to biological integration which use active materials for multiplexed addressing, local amplification and multimodal operation with high-channel-count and large-scale electronic systems in a safe fashion that provides multi-decade stable performance in both animal models and human subjects. The advances summarized in this review will guide the future direction of this technology and provide a basis for next-generation chronic neural interfaces with long-lived high-performance operation.

关键词： neural interface flexibility passive electrodes active recording barrier materials chronic stability

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Gaseous molecules-mediated electrochemical exfoliation of halogenated MXenes and its boosting in wear-resisting tribovoltaic devices

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Nature communications 2025年第1期16卷 5051页

作者： Qi Fan Minghua Chen Longyi Li Minghui Li Chuanxiao Xiao Tianci Zhao Long Pan Ningning Liang Qing Huang Lijing Yu Laipan Zhu Michael Naguib Kun Liang Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 P. R. China. University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing 100049 P. R. China. Beijing Key Laboratory of Micro-nano Energy and Sensor Center for High-Entropy Energy and Systems Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 P. R. China. Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 P. R. China. Ningbo New Materials Testing and Evaluation Center Co. Ltd Ningbo 315201 P. R. China. School of Physics and Optoelectronic Engineering Beijing University of Technology Beijing 100124 P. R. China. Key Laboratory of Advanced Metallic Materials of Jiangsu Province School of Materials Science and Engineering Southeast University Nanjing 211189 P. R. China. Qianwan Institute of CNITECH Ningbo 315201 P. R. China. Key Laboratory of Materials and Surface Technology (Ministry of Education) Sichuan Energy Equipment Intelligent Engineering Research Center School of Materials Science and Engineering Xihua University Chengdu 610039 P. R. China. ljyu@***. University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing 100049 P. R. China. zhulaipan@***. Beijing Key Laboratory of Micro-nano Energy and Sensor Center for High-Entropy Energy and Systems Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of Sciences Beijing 101400 P. R. China. zhulaipan@***. Department of Physics and Engineering Physics Tulane University New Orleans LA 70118 USA. Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sc

Two-dimensional transition metal carbides and/or nitrides (MXenes), especially their few-layered nanosheets, have triggered burgeoning research attentions owing to their superiorities including extraordinary electrical conductivity, accessible active surface, and adjustable processability. Molten salts etching route further achieves their controllable surface chemistry. However, the method encounters challenges in achieving few-layered structures due to more complex delamination behaviors. Herein, we present an efficient strategy to fabricate Cl- or Br-terminated MXene nanoflakes with few-layers, achieved by electrochemical intercalation of Li ions and concomitant solvent molecules from the electrolyte solution, with gaseous propylene molecules to disrupt interlayer forces. By controlling cut-off voltages, the optimal protocol results in nanosheets with a recovery rate of ~93% and preserved surface chemistry. The resultant MXenes dispersions were employed as lubricants to enhance tribovoltaic nanogenerators, where TiCBr displayed superior electrical output. These findings facilitate the understanding of MXenes' intrinsic physical properties and enable the nanoengineering of advanced electronic devices.

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Bidirectional LSTM-CRF for biomedical named entity recognition

Bidirectional LSTM-CRF for biomedical named entity recogniti...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Xuemin Yang Zhihong Gao Yongmin Li Chuandi Pan Ronggen Yang Lejun Gong Geng Yang Jiangsu Key Laboratory of Big Data Security &Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Zhejiang Engineering Research Center of Intelligence Medicine Wenzhou China Faculty Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

ISBN: (纸本)9781538680988;9781538680971

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